Abstract

NLG systems that generate natural language text from numerical input data must decide be- tween alternative surface linguistic forms for the natural language output. When using refer- ring expressions to identify numerical quantities, the system must decide between vague and crisp surface forms of the referring expression. Ideally, the system would be equipped with heuristics that it could use to make these decisions in the way that best suits the audience: however there is currrently little empirical data to draw on concerning the differential audience benefits of vague and crisp surface forms. In this paper we describe a series of experiments that investigate whether different surface forms affect the audience’s cognitive load in differ- ent ways. We estimate cognitive load by measuring the response latencies in a forced choice referent identification task in which we vary the surface form of the referring expression that constitutes the instruction in the task. We find that the pattern of audience responses across the series of experiments provides little support for the cost reduction hypothesis that vague surface forms should place fewer cogntive demands on the audience than crisp surface forms: instead the results support the view that referring expressions that contain numerals are more taxing for the audience than referring expressions that use natural language quantifiers, at least in the context of a forced choice referent identification task. We offer this work as an initial foray into the provision of heuristics to augment NLG systems with audience-sensitivity.